Generalized modeling of the effective thermal conductivity of particulate composites

Accurate and computationally efficient predictive models for the effective thermal conductivity of composites are needed to accelerate the design of new materials with improved properties and behavior. The predictive capabilities of previously developed models for particulate composites (PC) apply to limited ranges of component properties and proportions. Furthermore, existing material models that account for particle contiguity and filler-matrix thermal contact resistance fail to distinguish between those two effects. In this work, two novel and complementary predictive models for the effective thermal conductivity of two-phase isotropic particulate composites are derived: (i) a simple yet efficient analytical model for non-contiguous filler particles, and (ii) a generalized semi-analytical model accounting for both filler particle contiguity and thermal resistance at the filler-matrix interface. The latter model is powered by a thermal conduction grid solver algorithm that allows the incorporation of an unlimited number of elements and components to match increasingly complex particulate composite material configurations and behaviors. The two models proposed in this work can match previously published experimental data fairly well. The grid model is further leveraged to relate the effective thermal conductivity to filler particle size and size distribution. It is found that the formation of filler conduction chains is favored by well-graded particle size distributions.

Automated summary

This study introduces two novel predictive models for the effective thermal conductivity of two-phase isotropic particulate composites, including a simple analytical model and a generalized semi-analytical model considering both particle contiguity and thermal resistance. These models can match experimental data well and it is found that well-graded particle size distributions favor the formation of filler conduction chains.